Multi-influenced Factors Prediction Using a Combination of GEP and PCA-GRNN Model Based on the Shapley Value Method

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@InProceedings{Xia:2022:AIIPCC,

• author = "Chenxi Xia and Wanlu Dai and Dong Li and Fengling Li and Kai Liu and Zhijie He",
• booktitle = "2022 International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC)",
• title = "Multi-influenced Factors Prediction Using a Combination of {GEP} and {PCA-GRNN} Model Based on the Shapley Value Method",
• year = "2022",
• pages = "253--259",
• abstract = "The powerful function mining ability of the gene expression programming algorithm has obvious advantages in predicting dependent variables under multi-influence factors. PCA can obtain the key characteristics of the main influencing factors by the data reduction processing, and the generalised regression neural network can effectively overcome the disadvantage of slow training of the traditional neural network. Taking the cost of civil engineering in subway tunnels as an example, this paper introduces the Shapley value method for a combined prediction based on the prediction results of the above two optimal single prediction models and determines the weight of the two models based on the error of allocation. The results of the study show that the combined prediction model based on the Shapley value method reduces the error better than the single prediction model and further improves the prediction accuracy.",
• keywords = "genetic algorithms, genetic programming, gene expression programming, Training, Neural networks, ANN, Predictive models, Prediction algorithms, Mathematical models, Resource management, principal component analysis, PCS, generalised regression neural network, combination prediction, Shapley value method",
• DOI = "doi:10.1109/AIIPCC57291.2022.00062",
• month = aug,
• notes = "Also known as \cite{10070276}",

}

Genetic Programming entries for Chenxi Xia Wanlu Dai Dong Li Fengling Li Kai Liu Zhijie He

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